Particle swarm optimization in multilayer perceptron learning for well log data inversion

Kou-Yuan Huang*, Liang Chi Shen, Kai Ju Chen, Ming Che Huang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We adopt the multilayer perceptron (MLP) to approximate the nonlinear input-output mapping and propose the use of particle swarm optimization with mutation (MPSO) algorithm to adjust the weights in MLP. In the supervised training step, the input of the network is the apparent conductivity (Ca) and the desired output is the true formation conductivity (Ct). MLP with optimal size 10-9-10 is chosen as the model. We have experiment in simulation and real data application. In simulation, there are 31 sets of simulated well log data, where 25 sets are used for training, and 6 sets are used for testing. After training the MLP network, input Ca, then Ct' can be inverted in testing process. Also we apply it to the inversion of real field well log data. The result is acceptable. It shows that the proposed MPSO algorithm in MLP weight adjustments can perform the well log data inversion.

Original languageEnglish
Title of host publicationSociety of Exploration Geophysicists International Exposition and 82nd Annual Meeting 2012, SEG 2012
PublisherSociety of Exploration Geophysicists
Pages529-533
Number of pages5
ISBN (Print)9781622769452
DOIs
StatePublished - 1 Jan 2012
EventSociety of Exploration Geophysicists International Exposition and 82nd Annual Meeting 2012, SEG 2012 - Las Vegas, United States
Duration: 4 Nov 20129 Nov 2012

Publication series

NameSociety of Exploration Geophysicists International Exposition and 82nd Annual Meeting 2012, SEG 2012

Conference

ConferenceSociety of Exploration Geophysicists International Exposition and 82nd Annual Meeting 2012, SEG 2012
CountryUnited States
CityLas Vegas
Period4/11/129/11/12

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